1,177 research outputs found
MATEX: A Distributed Framework for Transient Simulation of Power Distribution Networks
We proposed MATEX, a distributed framework for transient simulation of power
distribution networks (PDNs). MATEX utilizes matrix exponential kernel with
Krylov subspace approximations to solve differential equations of linear
circuit. First, the whole simulation task is divided into subtasks based on
decompositions of current sources, in order to reduce the computational
overheads. Then these subtasks are distributed to different computing nodes and
processed in parallel. Within each node, after the matrix factorization at the
beginning of simulation, the adaptive time stepping solver is performed without
extra matrix re-factorizations. MATEX overcomes the stiff-ness hinder of
previous matrix exponential-based circuit simulator by rational Krylov subspace
method, which leads to larger step sizes with smaller dimensions of Krylov
subspace bases and highly accelerates the whole computation. MATEX outperforms
both traditional fixed and adaptive time stepping methods, e.g., achieving
around 13X over the trapezoidal framework with fixed time step for the IBM
power grid benchmarks.Comment: ACM/IEEE DAC 2014. arXiv admin note: substantial text overlap with
arXiv:1505.0669
Effects of red and blue light ratio on the morphological traits and flower sex expression in Cucurbita moschata Duch.
Squash (Cucurbita moschata Duch.) is an important fruit vegetable that can be long-term transport and storage. Light-emitting diodes (LEDs) are commercially used light sources applied to improve the producing of leaf vegetables in plant factory. However, the influences of LEDs on the plant growth and flower development of fruit vegetables remain unknown. In this study, five effective light quality treatments, including white light, a 10:8 ratio of blue (B) to red (R) light, a 10:4 mixture of blue/red light, red light, and blue light, were used for growing squash and inducing female flowers to maximize production. Our results show that varying light quality influence morphological traits and flower appearance. Both blue and red light improved the development of first and second internodes and induced larger leaves and petiole lengths, whereas 10:4 mixture caused shorter plant heights and decreased internode and petiole lengths. Although 10:8 mixture treatment reduced chlorophyll content, this spectral regime increased leaf number and influenced flower sex development, inducing more female flowers and more fruits. Light quality manipulation thus beneficially influences the growth and flower sex proportion in squash plants. Squash plants under 10:8 mixture treatment exhibited increase in yield, and can be used as a supplementary light treatment in plant factory
Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data
It is well known that recognizers personalized to each user are much more
effective than user-independent recognizers. With the popularity of smartphones
today, although it is not difficult to collect a large set of audio data for
each user, it is difficult to transcribe it. However, it is now possible to
automatically discover acoustic tokens from unlabeled personal data in an
unsupervised way. We therefore propose a multi-task deep learning framework
called a phoneme-token deep neural network (PTDNN), jointly trained from
unsupervised acoustic tokens discovered from unlabeled data and very limited
transcribed data for personalized acoustic modeling. We term this scenario
"weakly supervised". The underlying intuition is that the high degree of
similarity between the HMM states of acoustic token models and phoneme models
may help them learn from each other in this multi-task learning framework.
Initial experiments performed over a personalized audio data set recorded from
Facebook posts demonstrated that very good improvements can be achieved in both
frame accuracy and word accuracy over popularly-considered baselines such as
fDLR, speaker code and lightly supervised adaptation. This approach complements
existing speaker adaptation approaches and can be used jointly with such
techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201
Stationary Light Pulses in Cold Atomic Media
Stationary light pulses (SLPs), i.e., light pulses without motion, are formed
via the retrieval of stored probe pulses with two counter-propagating coupling
fields. We show that there exist non-negligible hybrid Raman excitations in
media of cold atoms that prohibit the SLP formation. We experimentally
demonstrate a method to suppress these Raman excitations and realize SLPs in
laser-cooled atoms. Our work opens the way to SLP studies in cold as well as in
stationary atoms and provides a new avenue to low-light-level nonlinear optics.Comment: 4 pages, 4 figure
Paging and Location Management in IEEE 802.16j Multihop Relay Network
IEEE 802.16j is an emerging wireless broadband
networking standard that integrates infrastructure base stations
with multihop relay technology. Based on the idle mode operation
in IEEE 802.16j, we propose a novel location management and
paging scheme. It integrates the paging area-based and the
timer-based location update mechanism. In paging area-based
scheme, an idle mode mobile station updates when it moves to
a new paging area. In timer-based scheme, an idle mode MS
updates when the location update timer expires. In this work, we
formulate the mathematical model to evaluate the performance of
the proposed paging scheme. A new random walk mobility model
that is suitable for modeling in multihop relay network is created.
Optimization of location update timer is also investigated
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